Optimizing microbiome reference databases with PacBio full-length 16S rRNA sequencing for enhanced taxonomic...

Area of Science:

• Microbiology
• Bioinformatics
• Genomics

Background:

• Human microbiome research is vital for understanding diseases and developing preventive strategies.
• 16S rRNA sequencing has advanced microbiome studies, but large reference databases present computational and accuracy challenges.
• Optimizing reference databases is crucial for accurate microbial analysis.

Purpose of the Study:

• To evaluate the utility of PacBio full-length 16S rRNA sequencing data for optimizing microbiome reference databases.
• To apply optimized reference databases to Illumina V3-V4 targeted sequencing data for improved microbial studies.

Main Methods:

• PacBio full-length 16S rRNA sequencing data were processed using DADA2 to generate amplicon sequencing variants (ASVs).
• ASVs were used to build and optimize reference databases, trained with the RDP database.
• QIIME2 was employed for analyzing Illumina V3-V4 targeted sequencing data, with BLAST and LEfSe for statistical analysis.

Main Results:

• PacBio-generated ASVs demonstrated comprehensive coverage of the oral microbiome.
• Trimming phylogenetic trees yielded optimized reference databases.
• Application to gut microbiome data showed enhanced taxa classification and biomarker discovery efficiency with the optimized database.

Conclusions:

• PacBio full-length 16S rRNA sequencing is effective for constructing microbiome reference databases.
• Optimized reference databases significantly improve microbiome classification accuracy and biomarker discovery.